# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
import unittest

import numpy as np
import paddle

from ppdiffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from ppdiffusers.pipelines.shap_e import ShapERenderer
from ppdiffusers.transformers import (
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
)
from ppdiffusers.utils import slow
from ppdiffusers.utils.testing_utils import require_paddle_gpu

from ..test_pipelines_common import PipelineTesterMixin


class ShapEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = ShapEPipeline
    params = ["prompt"]
    batch_params = ["prompt"]
    required_optional_params = [
        "num_images_per_prompt",
        "num_inference_steps",
        "generator",
        "latents",
        "guidance_scale",
        "frame_size",
        "output_type",
        "return_dict",
    ]

    @property
    def text_embedder_hidden_size(self):
        return 32

    @property
    def time_input_dim(self):
        return 32

    @property
    def time_embed_dim(self):
        return self.time_input_dim * 4

    @property
    def renderer_dim(self):
        return 8

    @property
    def dummy_tokenizer(self):
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        return tokenizer

    @property
    def dummy_text_encoder(self):
        paddle.seed(seed=0)
        config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=self.text_embedder_hidden_size,
            projection_dim=self.text_embedder_hidden_size,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=4,
            num_hidden_layers=5,
            pad_token_id=1,
            vocab_size=1000,
        )
        return CLIPTextModelWithProjection(config)

    @property
    def dummy_prior(self):
        paddle.seed(seed=0)
        model_kwargs = {
            "num_attention_heads": 2,
            "attention_head_dim": 16,
            "embedding_dim": self.time_input_dim,
            "num_embeddings": 32,
            "embedding_proj_dim": self.text_embedder_hidden_size,
            "time_embed_dim": self.time_embed_dim,
            "num_layers": 1,
            "clip_embed_dim": self.time_input_dim * 2,
            "additional_embeddings": 0,
            "time_embed_act_fn": "gelu",
            "norm_in_type": "layer",
            "encoder_hid_proj_type": None,
            "added_emb_type": None,
        }
        model = PriorTransformer(**model_kwargs)
        return model

    @property
    def dummy_renderer(self):
        paddle.seed(seed=0)
        model_kwargs = {
            "param_shapes": (
                (self.renderer_dim, 93),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            "d_latent": self.time_input_dim,
            "d_hidden": self.renderer_dim,
            "n_output": 12,
            "background": (0.1, 0.1, 0.1),
        }
        model = ShapERenderer(**model_kwargs)
        return model

    def get_dummy_components(self):
        prior = self.dummy_prior
        text_encoder = self.dummy_text_encoder
        tokenizer = self.dummy_tokenizer
        shap_e_renderer = self.dummy_renderer
        scheduler = HeunDiscreteScheduler(
            beta_schedule="exp",
            num_train_timesteps=1024,
            prediction_type="sample",
            use_karras_sigmas=True,
            clip_sample=True,
            clip_sample_range=1.0,
        )
        components = {
            "prior": prior,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "shap_e_renderer": shap_e_renderer,
            "scheduler": scheduler,
        }
        return components

    def get_dummy_inputs(self, seed=0):
        generator = paddle.Generator().manual_seed(seed)
        inputs = {
            "prompt": "horse",
            "generator": generator,
            "num_inference_steps": 1,
            "frame_size": 32,
            "output_type": "latent",  # random state in ShapERenderer, we need output latent
        }
        return inputs

    def test_shap_e(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        inputs = self.get_dummy_inputs()
        inputs["output_type"] = "np"
        output = pipe(**inputs)
        image = output.images[0]
        image_slice = image[(0), -3:, -3:, (-1)]
        assert image.shape == (20, 32, 32, 3)
        expected_slice = np.array(
            [
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
            ]
        )
        assert np.abs(image_slice.flatten() - expected_slice).max() < 0.01

    def test_inference_batch_consistent(self):
        self._test_inference_batch_consistent(batch_sizes=[1, 2])

    def test_inference_batch_single_identical(self):
        test_max_difference = False
        relax_max_difference = True
        self._test_inference_batch_single_identical(
            batch_size=2, test_max_difference=test_max_difference, relax_max_difference=relax_max_difference
        )

    def test_num_images_per_prompt(self):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.set_progress_bar_config(disable=None)
        batch_size = 1
        num_images_per_prompt = 2
        inputs = self.get_dummy_inputs()
        for key in inputs.keys():
            if key in self.batch_params:
                inputs[key] = batch_size * [inputs[key]]
        images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
        assert images.shape[0] == batch_size * num_images_per_prompt

    def test_save_load_float16(self):
        # fix this in 0.0.0 paddlepaddle
        pass

    def test_xformers_attention_forwardGenerator_pass(self):
        pass


@slow
@require_paddle_gpu
class ShapEPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        paddle.device.cuda.empty_cache()

    # def test_shap_e(self):
    #     expected_image = load_numpy(
    #         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/shap_e/test_shap_e_np_out.npy"
    #     )
    #     pipe = ShapEPipeline.from_pretrained("openai/shap-e")
    #     pipe.set_progress_bar_config(disable=None)
    #     generator = paddle.Generator().manual_seed(0)
    #     images = pipe(
    #         "a shark",
    #         generator=generator,
    #         guidance_scale=15.0,
    #         num_inference_steps=64,
    #         frame_size=64,
    #         output_type="np",
    #     ).images[0]
    #     assert images.shape == (20, 64, 64, 3)
    #     assert_mean_pixel_difference(images, expected_image)
